The present invention relates generally to semiconductor metrology. More specifically, it relates to optimization procedures for setting up a metrology tool for optimum performance during a subsequent metrology process.
Generally, the industry of semiconductor manufacturing involves highly complex techniques for fabricating integrating circuits using semiconductor materials which are layered and patterned onto a substrate, such as silicon. Due to the large scale of circuit integration and the decreasing size of semiconductor devices, the device must comply with rigorous specification requirements prior to shipment of the device to the end users or customers.
Typically, particular parameters are measured within special test structures or targets using a metrology tool. By way of example, various targets are designed to measure misalignment or overlay errors between two adjacent layers. The measurement of overlay error between successive patterned layers on a wafer is one of the most critical process control techniques used in the manufacturing of integrated circuits and devices. Overlay accuracy generally pertains to the determination of how accurately a first patterned layer aligns with respect to a second patterned layer disposed above or below it and to the determination of how accurately a first pattern aligns with respect to a second pattern disposed on the same layer. Presently, overlay measurements are performed via test patterns that are printed together with layers of the wafer. The images of these test patterns are captured via an imaging metrology tool and an analysis algorithm is used to calculate the relative displacement of the patterns from the captured images.
The most commonly used overlay target pattern is the “Box-in-Box” target, which includes a pair of concentric squares (or boxes) that are formed on successive layers of the wafer. The overlay error is generally determined by comparing the position of one square relative to another square.
To facilitate discussion, FIG. 1 is a top view of a typical “Box-in-Box” target 10. As shown, the target 10 includes an inner box 12 disposed within an open-centered outer box 14. The inner box 12 is printed on the top layer of the wafer while the outer box 14 is printed on the layer directly below the top layer of the wafer. As is generally well known, the overlay error between the two boxes, along the x-axis for example, is determined by calculating the locations of the edges of lines c1 and c2 of the outer box 14, and the edge locations of the lines c3 and c4 of the inner box 12, and then comparing the average separation between lines c1 and c3 with the average separation between lines c2 and c4. Half of the difference between the average separations c1&c3 and c2&c4 is the overlay error (along the x-axis). Thus, if the average spacing between lines c1 and c3 is the same as the average spacing between lines c2 and c4, the corresponding overlay error tends to be zero. Although not described, the overlay error between the two boxes along the y-axis may also be determined using the above technique.
Prior to making the above described measurements to determine overlay error, the metrology tool needs to be optimized. The tool is optimized by manually adjusting operating parameters (such as focus settings, algorithm selection, etc.) so as to achieve an image having optimum characteristics, such as a best contrast, etc. For instance, if the tool is used to image a particular kernel or region of interest (ROI) of the target (e.g., 16 or 18), an operator typically adjusts an imaging tool until it most closely matches what the operator perceives as an ideal image, such as the idealized image whose gray levels across the kernel in an x direction are shown in FIG. 2. Various operating parameters are continuously adjusted by an operator until the tool is optimized for a particular target. This optimization procedure is repeated for different target types.
Unfortunately, conventional optimization procedures are rather time consuming since they are performed manually, requiring significant man hours to adequately adjust the tool for various targets. Additionally, when several operators perform optimization on a tool or different tools, performance of such tool(s) is not consistent since different operators will achieve a different optimization result and the same operator may achieve different optimization results a different times for the same tool and target type.
In light of the above, it would be beneficial to implement an optimization procedure so that peak performance is consistently achieved over time for the same tool, as well as different tools. For example, it would be highly desirable to consistently imitate the most experienced application engineer's process for optimization in an automated optimization process.